Severe acquired subglottic stenosis in neonatal intensive care graduates: a case–control study
Bibliographic record
Abstract
OBJECTIVE: To analyse current incidence and risk factors associated with severe acquired subglottic stenosis (SASGS) requiring surgical intervention in neonates. DESIGN: Retrospective case-control study. SETTING: Sole tertiary children's hospital. PARTICIPANTS: Patients who underwent surgical intervention for SASGS from January 2006 to December 2014. For each neonatal intensive care unit (NICU) graduate with acquired SASGS, two controls were selected (matched for gestation and year of birth). MAIN OUTCOMES AND MEASURES: Incidences were calculated and cases and controls compared using conditional logistic regression analysis to identify risk factors for SASGS. RESULTS: Thirty-seven NICU graduates required surgical intervention for SASGS of whom 35 were <30-week gestation at birth. The incidence of SASGS in surviving children who had required ventilation in the neonatal period was 27/2913 (0.93%). Incidence was higher in infants <28-week gestation (24/623=3.8%) compared with infants ≥28-week gestation (3/2290=0.13%; p=0.0001). On univariate analysis, risk factors for SASGS were: higher number of intubations (4 vs 2; p<0.001); longer duration ventilation (16 vs 9.5 days; p<0.001); unplanned extubation (45.7% vs 20.0%; p=0.007); traumatic intubation (34.3% vs 7.1%; p=0.003) and oversized endotracheal tubes (ETTs) (74.3% vs 42.9%; p=0.001). On multivariate analysis, risk factors for SASGS were: Sherman ratio >0.1 (adjusted OR (aOR) 6.40; 95% CI 1.65 to 24.77); more than five previous intubations (aOR 3.74; 95% CI 1.15 to 12.19); traumatic intubation (aOR 3.37; 95% CI 1.01 to 11.26). CONCLUSIONS: SASGS is a serious consequence of intubation for mechanical ventilation in NICU graduates, especially in preterm infants. Minimising trauma during intubations, avoiding recurrent extubation/reintubations and using appropriate sized ETTs may help prevent this serious complication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".